5 papers
DANCE: Density-agnostic and Class-aware Network for Point Cloud Completion
Da-Yeong Kim, Yeong-Jun Cho
Point cloud completion aims to recover missing geometric structures from incomplete 3D scans, which often suffer from occlusions or limited sensor viewpoints. Existing methods typi…
CSF-Net: Context-Semantic Fusion Network for Large Mask Inpainting
Chae-Yeon Heo, Yeong-Jun Cho
In this paper, we propose a semantic-guided framework to address the challenging problem of large-mask image inpainting, where essential visual content is missing and contextual cu…
Med-SORA: Symptom to Organ Reasoning in Abdomen CT Images
You-Kyoung Na, Yeong-Jun Cho
Understanding symptom-image associations is crucial for clinical reasoning. However, existing medical multimodal models often rely on simple one-to-one hard labeling, oversimplifyi…
PointCubeNet: 3D Part-level Reasoning with 3x3x3 Point Cloud Blocks
Da-Yeong Kim, Yeong-Jun Cho
In this paper, we propose PointCubeNet, a novel multi-modal 3D understanding framework that achieves part-level reasoning without requiring any part annotations. PointCubeNet compr…
NOVO: Bridging LLaVA and SAM with Visual-only Prompts for Reasoning Segmentation
Kyung-Yoon Yoon, Yeong-Jun Cho
In this study, we propose NOVO (NO text, Visual-Only prompts), a novel framework that bridges vision-language models (VLMs) and segmentation models through visual-only prompts. Unl…